• Journal of KIISE:Databases
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• v.35 no.6
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• pp.459-468
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• 2008

A data stream which is a massive unbounded sequence of data elements continuously generated at a rapid rate. Many recent research activities for emerging applications often need to deal with the data stream. Such applications can be web click monitoring, sensor data processing, network traffic analysis. telephone records and multi-media data. For this. data processing over a data stream are not performed on the stored data but performed the newly updated data with pre-registered queries, and then return a result immediately or periodically. Recently, many studies are focused on dealing with a data stream more than a stored data set. Especially. there are many researches to optimize continuous queries in order to perform them efficiently. This paper proposes a query optimization algorithm to manage continuous query which has multiple join operators(Multi-way join) over data streams. It is called by an Extended Greedy query optimization based on a greedy algorithm. It defines a join cost by a required operation to compute a join and an operation to process a result and then stores all information for computing join cost and join cost in the statistics catalog. To overcome a weak point of greedy algorithm which has poor performance, the algorithm selects the set of operators with a small lay, instead of operator with the smallest cost. The set is influenced the accuracy and execution time of the algorithm and can be controlled adaptively by two user-defined values. Experiment results illustrate the performance of the EGA algorithm in various stream environments.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.5-6
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• 2008

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.164-165
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• 2010

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.27-28
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• 2010

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.126-132
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• 2011

In the application areas of LBS(Location Based Service) and ITS(Intelligent Transportation System), continuous k-nearest neighbor query(CkNN) which is defined as a query to find the nearest points of interest to all the points on a given path is widely used. It is necessary to acquire results quickly in the above applications and be applicable to spatial network databases. It is also able to cope successfully with frequent updates of POI objects. This paper proposes a new method to search nearest POIs for moving query objects on the spatial networks. The method produces a set of split points and their corresponding k-POIs as results with partial order among k-POIs. The results obtained from experiments with real dataset show that the proposed method outperforms the existing methods. The proposed method achieves very short processing time(15%) compared with the existing method.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.1-16
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• 2008

Join queries having heavy cost are necessary to Data Stream Management System in Sensor Network where plural short information is generated. It is reasonable that each join operator has a sliding-window constraint for preventing DISK I/O because the data stream represents the infinite size of data. In addition, the join operator should be able to take multiple inputs for overall results. It is possible for the MJoin operator with sliding-windows to do so. In this paper, we consider the data stream environment where multiple MJoin operators are registered and propose MMJoin which deals with issues of building and processing a globally shared query considering characteristics of the MJoin operator with sliding-windows. First, we propose a solution of building the global shared query execution plan. Second, we solved the problems of updating a window size and routing for a join result. Our study can be utilized as a fundamental research for an optimization technique for multiple continuous joins in the data stream environment.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.35-44
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• 2009

The emerging notion of data stream has brought many new challenges to the research communities as a consequence of its conceptual difference with conventional concepts of just data. One typical example is data stream processing in sensor networks. The range of data processing considerations in a sensor network is very wide, from physical resource restrictions such as bandwidth, energy, and memory to the peculiarities of query processing including continuous and specific types of queries. In this paper, as one of the physical constraints in data stream processing, we consider the problem of limited memory and propose a new scheme for data stream reduction based on the Principal Component Analysis (PCA) technique. PCA can transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. We adapt PCA for the data stream of a sensor network assuming the cooperation of a query engine (or application) with a network base station. Our method exploits the spatio-temporal correlation among multiple measurements from different sensors. Finally, we present a new framework for data processing and describe a number of experiments under this framework. We compare our scheme with the wavelet transform and observe the effect of time stamps on the compression ratio. We report on some of the results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.794-800
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• 2010

It is necessary to accurate and efficient management for measured digital data from various sensors in digital marine vessel. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. In this paper, We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose that we arrange some sensors (temperature, humidity, lighting, voice) and process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using SVM algorithm. We automatically delete that it isn't necessary to the data from the database and we used to ship diagnosis system for available data. As a result, we obtained to efficient result about 18.3% reduction rate of database using 35,912 data sets.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.307-318
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• 2008

One of the primary issues confronting XML message brokers is the difficulty associated with processing a large set of continuous XPath queries over incoming XML seams. This paper proposes a novel system designed to present an effective solution to this problem. The proposed system transforms multiple XPath queries before their run-time into a new region-based data structure, called an XP-table, by sharing their common constraints. An XP-table is matched with a stream relation (SR) transformed from a target XML stream by a SAX parser. This arrangement is intended to minimize the runtime workload of continuous query processing. Also, system performance is estimated and verified through a variety of experiments, including comparisons with previous approaches such as YFilter and LazyDFA. The proposed system is practically linear- scalable and stable for evaluating a set of XPath queries in a continuous and timely fashion.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.43-48
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• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.